Meta-Learning with Graph Neural Networks: Methods and Applications

Mandal, Debmalya; Medya, Sourav; Uzzi, Brian; Aggarwal, Charų C.

doi:10.48550/arxiv.2103.00137

Cited by 4 publications

(3 citation statements)

References 21 publications

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“…Meta-learning has been adopted over graph data to deal with various graph learning tasks, including node classification [19,47], link prediction [5,19], graph classification [7,29] and graph alignment [43,46]. A brief survey that summarizes the applications and methods can be found in [30].…”

Section: Related Workmentioning

confidence: 99%

Community Search: A Meta-Learning Approach

Fang¹,

Zhao²,

Li³

et al. 2022

Preprint

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Community Search (CS) is one of the fundamental graph analysis tasks, which is a building block of various real applications. Given any query nodes, CS aims to find cohesive subgraphs that query nodes belong to. Recently, a large number of CS algorithms are designed. These algorithms adopt pre-defined subgraph patterns to model the communities, which cannot find communities that do not have such pre-defined patterns in real-world graphs. Thereby, machine learning based approaches are proposed to capture flexible community structures by learning from community ground-truth in a data-driven fashion. However, existing approaches rely on sufficient training data to provide enough generalization for machine learning models.In this paper, we study ML-based approaches for community search, under the circumstance that the training data is scarce. To learn from small data, we extract prior knowledge which is shared across different graphs as CS tasks in advance. Subsequent small training data from a new CS task are combined with the learned prior knowledge to help the model well adapt to that specific task. A novel meta-learning based framework, called CGNP, is designed and implemented to fulfill this learning procedure. A meta CGNP model is a task-common node embedding function for clustering by nature, learned by metric-based learning. To the best of our knowledge, CGNP is the first meta model solution for CS. We compare CGNP with traditional CS algorithms, e.g., CTC, ATC, ACQ, and ML baselines on real graph datasets with groundtruth. Our experiments show that CGNP outperforms the native graph algorithms and ML baselines 147% and 113% on F1-score by average.

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Section: Related Workmentioning

confidence: 99%

Community Search: A Meta-Learning Approach

Fang¹,

Zhao²,

Li³

et al. 2022

Preprint

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“…Recently, several meta learning methods to train GNNs have been proposed to solve the limited samples problem [321]. Most of the existing works [322]- [327] adopt the Model-Agnostic Meta-Learning (MAML) algorithm [261].…”

Section: Gnn With Meta Learningmentioning

confidence: 99%

Edge-Cloud Polarization and Collaboration: A Comprehensive Survey for AI

Yao¹,

Zhang²,

Yang³

et al. 2021

Preprint

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Influenced by the great success of deep learning via cloud computing and the rapid development of edge chips, research in artificial intelligence (AI) has shifted to both of the computing paradigms, i.e., cloud computing and edge computing. In recent years, we have witnessed significant progress in developing more advanced AI models on cloud servers that surpass traditional deep learning models owing to model innovations (e.g., Transformers, Pretrained families), explosion of training data and soaring computing capabilities. However, edge computing, especially edge and cloud collaborative computing, are still in its infancy to announce their success due to the resource-constrained IoT scenarios with very limited algorithms deployed. In this survey, we conduct a systematic review for both cloud and edge AI. Specifically, we are the first to set up the collaborative learning mechanism for cloud and edge modeling with a thorough review of the architectures that enable such mechanism. We also discuss potentials and practical experiences of some on-going advanced edge AI topics including pretraining models, graph neural networks and reinforcement learning. Finally, we discuss the promising directions and challenges in this field.

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“…The learned GNN might bias towards performing well on edge prediction task, but downgrades on the other tasks like node clustering or classification. Recently some works (Mandal et al 2021) attempt to bring the idea of meta-learning to train GNNs with meta knowledge which can help to avoid the bias caused by single pretext task. However, the meta-GNN might contain knowledge that cause task discrepancy issue .…”

Section: Introductionmentioning

confidence: 99%

SAIL: Self-Augmented Graph Contrastive Learning

Pei

Ding

et al. 2022

AAAI

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This paper studies learning node representations with graph neural networks (GNNs) for unsupervised scenario. Specifically, we derive a theoretical analysis and provide an empirical demonstration about the non-steady performance of GNNs over different graph datasets, when the supervision signals are not appropriately defined. The performance of GNNs depends on both the node feature smoothness and the locality of graph structure. To smooth the discrepancy of node proximity measured by graph topology and node feature, we proposed SAIL - a novel self-augmented graph contrastive learning framework, with two complementary self-distilling regularization modules, i.e., intra- and inter-graph knowledge distillation. We demonstrate the competitive performance of SAIL on a variety of graph applications. Even with a single GNN layer, SAIL has consistently competitive or even better performance on various benchmark datasets, comparing with state-of-the-art baselines.

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Meta-Learning with Graph Neural Networks: Methods and Applications

Cited by 4 publications

References 21 publications

Community Search: A Meta-Learning Approach

Community Search: A Meta-Learning Approach

Edge-Cloud Polarization and Collaboration: A Comprehensive Survey for AI

SAIL: Self-Augmented Graph Contrastive Learning

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